Predicting conductance due to upconing using neural networks.

Artificial neural networks (ANNs) were developed to accurately predict highly time-variable specific conductance values in an unconfined coastal aquifer. Conductance values in the fresh water lens aquifer change in response to vertical displacements of the brackish zone and fresh water-salt water interface, which are caused by variable pumping and climate conditions. Unlike physical-based models, which require hydrologic parameter inputs, such as horizontal and vertical hydraulic conductivities, porosity, and fluid densities, ANNs can "learn" system behavior from easily measurable variables.

Mobilization of natural colloids from an iron oxide-coated sand aquifer: effect of pH and ionic strength.

Field and laboratory column experiments were performed to assess the effect of elevated pH and reduced ionic strength on the mobilization of natural colloids in a ferric oxyhydroxide-coated aquifer sediment. The field experiments were conducted as natural gradient injections of groundwater amended by sodium hydroxide additions. The laboratory experiments were conducted in columns of undisturbed, oriented sediments and disturbed, disoriented sediments.